Secure & Personalized Music-to-Video Generation via CHARCHA
Music is a deeply personal experience and our aim is to enhance this with a fully-automated pipeline for personalized music video generation. Our work allows listeners to not just be consumers but co-creators in the music video generation process by creating personalized, consistent and context-driven visuals based on lyrics, rhythm and emotion in the music. The pipeline combines multimodal translation and generation techniques and utilizes low-rank adaptation on listeners' images to create immersive music videos that reflect both the music and the individual. To ensure the ethical use of users' identity, we also introduce CHARCHA (patent pending), a facial identity verification protocol that protects people against unauthorized use of their face while at the same time collecting authorized images from users for personalizing their videos. This paper thus provides a secure and innovative framework for creating deeply personalized music videos.
Code (0)
등록된 구현이 없습니다.
Tasks
RhythmVideo GenerationSimilar Papers 제목 키워드 기반
Every Image Listens, Every Image Dances: Music-Driven Image Animation
Image animation has become a promising area in multimodal research, with a focus on generating videos from reference images. While prior work has largely emphasized generic video generation guided by text, music-driven d…
Image AnimationVideo GenerationA Training-Free Approach for Music Style Transfer with Latent Diffusion Models
Music style transfer, while offering exciting possibilities for personalized music generation, often requires extensive training or detailed textual descriptions. This paper introduces a novel training-free approach leve…
Music GenerationMusic Style TransferStyle TransferVideo Background Music Generation: Dataset, Method and Evaluation
Music is essential when editing videos, but selecting music manually is difficult and time-consuming. Thus, we seek to automatically generate background music tracks given video input. This is a challenging task since it…
Music GenerationRepresentation LearningRetrievalSingDance: Compositional Zero-Shot Singing-and-Dancing Video Generation with Role-Aware Audio Conditioning
Generating personalized dance videos from a reference image, text prompt, and audio track requires music-conditioned body motion. Singing-and-dancing adds a second requirement: the visible subject must also articulate th…
Video GenerationVMAS: Video-to-Music Generation via Semantic Alignment in Web Music Videos
We present a framework for learning to generate background music from video inputs. Unlike existing works that rely on symbolic musical annotations, which are limited in quantity and diversity, our method leverages large…
Contrastive LearningMusic Generation